


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)


def       UpperCAmelCase_					(   UpperCAmelCase__	):

					if model.config.model_type == "gpt2":
										return model.transformer.h[0].mlp.c_fc
					return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
							import torch
							import torch.nn as nn







							class 							UpperCamelCase__ (						nn.Module ):

												def __init__(  self							: Optional[Any]				,       UpperCamelCase__							: nn.Module				,       UpperCamelCase__							: int							):






																	'''simple docstring'''






																	super().__init__()
																	lowercase_					=      module
																	lowercase_					=      nn.Sequential(
																	    nn.Linear(module.in_features				,       lowercase_				,       bias=lowercase_							)				,       nn.Linear(lowercase_				,       module.out_features				,       bias=lowercase_							)				,       )
																	lowercase_					=      (2.0 / (5 * min(module.in_features				,       module.out_features							))) ** 0.5
																	nn.init.normal_(self.adapter[0].weight				,       std=lowercase_							)
																	nn.init.zeros_(self.adapter[1].weight							)
																	self.adapter.to(module.weight.device							)







												def 	UpperCAmelCase__							(  self							: str				,       UpperCamelCase__							: Union[str, Any]				,       *UpperCamelCase__							: Optional[Any]				,       **UpperCamelCase__							: List[Any]							):






																	'''simple docstring'''






																	return self.module(lowercase_				,       *lowercase_				,       **lowercase_							) + self.adapter(lowercase_							)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							UpperCamelCase__ (						unittest.TestCase ):
					__SCREAMING_SNAKE_CASE							:	List[Any]            = 'bigscience/bloom-1b7'

					# Constant values
					__SCREAMING_SNAKE_CASE							:	Dict            = 2.1_0_9_6_5_9_5_5_2_6_9_2_5_7_4

					__SCREAMING_SNAKE_CASE							:	int            = 'Hello my name is'
					__SCREAMING_SNAKE_CASE							:	Optional[int]            = set()
					EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I' )
					EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n' )
					EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University' )
					__SCREAMING_SNAKE_CASE							:	Tuple            = 10

					def 	UpperCAmelCase__							(  self							: str							):






										'''simple docstring'''






										lowercase_					=      AutoTokenizer.from_pretrained(self.model_name							)







class 							UpperCamelCase__ (						__magic_name__ ):

					def 	UpperCAmelCase__							(  self							: Optional[int]							):






										'''simple docstring'''






										super().setUp()

										# Models and tokenizer
										lowercase_					=      AutoModelForCausalLM.from_pretrained(
										    self.model_name				,       torch_dtype=torch.floataa				,       device_map="""auto"""							)
										lowercase_					=      AutoModelForCausalLM.from_pretrained(self.model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)

					def 	UpperCAmelCase__							(  self							: Tuple							):






										'''simple docstring'''






										del self.model_fpaa
										del self.model_abit

										gc.collect()
										torch.cuda.empty_cache()

					def 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										lowercase_					=      self.model_abit.config

										self.assertTrue(hasattr(lowercase_				,       """quantization_config"""							)							)

										lowercase_					=      config.to_dict()
										lowercase_					=      config.to_diff_dict()

										lowercase_					=      config.to_json_string()

					def 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										from bitsandbytes.nn import Paramsabit

										lowercase_					=      self.model_fpaa.get_memory_footprint()
										lowercase_					=      self.model_abit.get_memory_footprint()

										self.assertAlmostEqual(mem_fpaa / mem_abit				,       self.EXPECTED_RELATIVE_DIFFERENCE							)
										lowercase_					=      get_some_linear_layer(self.model_abit							)
										self.assertTrue(linear.weight.__class__ == Paramsabit							)

					def 	UpperCAmelCase__							(  self							: Optional[int]							):






										'''simple docstring'''






										from transformers import TaPreTrainedModel

										self.model_fpaa.get_memory_footprint()
										self.model_abit.get_memory_footprint()

										for name, module in self.model_abit.named_modules():
															if isinstance(lowercase_				,       torch.nn.Linear							):
																				if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
																									# 4-bit parameters are packed in uint8 variables
																									self.assertTrue(module.weight.dtype == torch.uinta							)

					def 	UpperCAmelCase__							(  self							: Tuple							):






										'''simple docstring'''






										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							)
										lowercase_					=      self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0							)				,       max_new_tokens=10							)

										self.assertIn(self.tokenizer.decode(output_sequences[0]				,       skip_special_tokens=lowercase_							)				,       self.EXPECTED_OUTPUTS							)

					def 	UpperCAmelCase__							(  self							: Tuple							):






										'''simple docstring'''






										lowercase_					=      BitsAndBytesConfig()
										lowercase_					=      True

										lowercase_					=      AutoModelForCausalLM.from_pretrained(
										    self.model_name				,       quantization_config=lowercase_				,       device_map="""auto"""							)

										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							)
										lowercase_					=      model_abit_from_config.generate(
										    input_ids=encoded_input["""input_ids"""].to(0							)				,       max_new_tokens=10							)

										self.assertIn(self.tokenizer.decode(output_sequences[0]				,       skip_special_tokens=lowercase_							)				,       self.EXPECTED_OUTPUTS							)

					def 	UpperCAmelCase__							(  self							: Union[str, Any]							):






										'''simple docstring'''






										with self.assertRaises(lowercase_							), tempfile.TemporaryDirectory() as tmpdirname:
															self.model_abit.save_pretrained(lowercase_							)

					def 	UpperCAmelCase__							(  self							: List[Any]							):






										'''simple docstring'''






										lowercase_					=      BitsAndBytesConfig()

										with self.assertRaises(lowercase_							):
															lowercase_					=      AutoModelForCausalLM.from_pretrained(
															    self.model_name				,       quantization_config=lowercase_				,       load_in_abit=lowercase_				,       device_map="""auto"""				,       bnb_abit_quant_type="""nf4"""				,       )

					def 	UpperCAmelCase__							(  self							: Optional[Any]							):






										'''simple docstring'''






										with self.assertRaises(lowercase_							):
															# Tries with `str`
															self.model_abit.to("""cpu"""							)

										with self.assertRaises(lowercase_							):
															# Tries with a `dtype``
															self.model_abit.to(torch.floataa							)

										with self.assertRaises(lowercase_							):
															# Tries with a `device`
															self.model_abit.to(torch.device("""cuda:0"""							)							)

										with self.assertRaises(lowercase_							):
															# Tries with a `device`
															self.model_abit.float()

										with self.assertRaises(lowercase_							):
															# Tries with a `device`
															self.model_abit.half()

										# Test if we did not break anything
										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							)

										lowercase_					=      self.model_fpaa.to(torch.floataa							)
										lowercase_					=      self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0							)				,       max_new_tokens=10							)

										# Check this does not throw an error
										lowercase_					=      self.model_fpaa.to("""cpu"""							)

										# Check this does not throw an error
										lowercase_					=      self.model_fpaa.half()

										# Check this does not throw an error
										lowercase_					=      self.model_fpaa.float()







					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										lowercase_					=      AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""				,       load_in_abit=lowercase_				,       device_map="""auto"""							)
										self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							UpperCamelCase__ (						unittest.TestCase ):

					@classmethod
					def 	UpperCAmelCase__							(  cls							: List[str]							):






										'''simple docstring'''






										lowercase_					=      """t5-small"""
										lowercase_					=      """google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
										lowercase_					=      AutoTokenizer.from_pretrained(cls.model_name							)
										lowercase_					=      """Translate in German: Hello, my dog is cute"""

					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										gc.collect()
										torch.cuda.empty_cache()

					def 	UpperCAmelCase__							(  self							: Optional[Any]							):






										'''simple docstring'''






										from transformers import TaForConditionalGeneration

										lowercase_					=      TaForConditionalGeneration._keep_in_fpaa_modules
										lowercase_					=      None

										# test with `t5-small`
										lowercase_					=      TaForConditionalGeneration.from_pretrained(self.model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)
										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							).to(0							)
										lowercase_					=      model.generate(**lowercase_							)

										# test with `flan-t5-small`
										lowercase_					=      TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)
										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							).to(0							)
										lowercase_					=      model.generate(**lowercase_							)
										lowercase_					=      modules







					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										import bitsandbytes as bnb

										from transformers import TaForConditionalGeneration

										# test with `t5-small`
										lowercase_					=      TaForConditionalGeneration.from_pretrained(self.model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)

										# there was a bug with decoders - this test checks that it is fixed
										self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q				,       bnb.nn.Linearabit							)							)

										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							).to(0							)
										lowercase_					=      model.generate(**lowercase_							)

										# test with `flan-t5-small`
										lowercase_					=      TaForConditionalGeneration.from_pretrained(
										    self.dense_act_model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)
										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							).to(0							)
										lowercase_					=      model.generate(**lowercase_							)







class 							UpperCamelCase__ (						__magic_name__ ):

					def 	UpperCAmelCase__							(  self							: Optional[int]							):






										'''simple docstring'''






										super().setUp()
										# model_name
										lowercase_					=      """bigscience/bloom-560m"""
										lowercase_					=      """t5-small"""

										# Different types of model

										lowercase_					=      AutoModel.from_pretrained(self.model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)
										# Sequence classification model
										lowercase_					=      AutoModelForSequenceClassification.from_pretrained(
										    self.model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)
										# CausalLM model
										lowercase_					=      AutoModelForCausalLM.from_pretrained(self.model_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)
										# Seq2seq model
										lowercase_					=      AutoModelForSeqaSeqLM.from_pretrained(
										    self.seq_to_seq_name				,       load_in_abit=lowercase_				,       device_map="""auto"""							)

					def 	UpperCAmelCase__							(  self							: Union[str, Any]							):






										'''simple docstring'''






										del self.base_model
										del self.sequence_model
										del self.model_abit
										del self.seq_to_seq_model

										gc.collect()
										torch.cuda.empty_cache()







					def 	UpperCAmelCase__							(  self							: Union[str, Any]							):






										'''simple docstring'''






										from bitsandbytes.nn import Paramsabit

										self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

										# Other heads should be nn.Parameter
										self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
										self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
										self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)







class 							UpperCamelCase__ (						__magic_name__ ):

					def 	UpperCAmelCase__							(  self							: Union[str, Any]							):






										'''simple docstring'''






										super().setUp()

					def 	UpperCAmelCase__							(  self							: str							):






										'''simple docstring'''






										del self.pipe

										gc.collect()
										torch.cuda.empty_cache()







					def 	UpperCAmelCase__							(  self							: Dict							):






										'''simple docstring'''






										lowercase_					=      pipeline(
										    """text-generation"""				,       model=self.model_name				,       model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}				,       max_new_tokens=self.MAX_NEW_TOKENS				,       )

										# Real second forward pass
										lowercase_					=      self.pipe(self.input_text							)
										self.assertIn(pipeline_output[0]["""generated_text"""]				,       self.EXPECTED_OUTPUTS							)







@require_torch_multi_gpu
class 							UpperCamelCase__ (						__magic_name__ ):

					def 	UpperCAmelCase__							(  self							: Any							):






										'''simple docstring'''






										super().setUp()







					def 	UpperCAmelCase__							(  self							: str							):






										'''simple docstring'''






										lowercase_					=      AutoModelForCausalLM.from_pretrained(
										    self.model_name				,       load_in_abit=lowercase_				,       device_map="""balanced"""							)

										# Check correct device map
										self.assertEqual(set(model_parallel.hf_device_map.values()							)				,       {0, 1}							)

										# Check that inference pass works on the model
										lowercase_					=      self.tokenizer(self.input_text				,       return_tensors="""pt"""							)

										# Second real batch
										lowercase_					=      model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0							)				,       max_new_tokens=10							)
										self.assertIn(self.tokenizer.decode(output_parallel[0]				,       skip_special_tokens=lowercase_							)				,       self.EXPECTED_OUTPUTS							)







class 							UpperCamelCase__ (						__magic_name__ ):

					def 	UpperCAmelCase__							(  self							: Tuple							):






										'''simple docstring'''






										lowercase_					=      """facebook/opt-350m"""
										super().setUp()







					def 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										if version.parse(importlib.metadata.version("""bitsandbytes"""							)							) < version.parse("""0.37.0"""							):
															return

										# Step 1: freeze all parameters
										lowercase_					=      AutoModelForCausalLM.from_pretrained(self.model_name				,       load_in_abit=lowercase_							)

										self.assertEqual(set(model.hf_device_map.values()							)				,       {torch.cuda.current_device()}							)

										for param in model.parameters():
															lowercase_					=      False  # freeze the model - train adapters later
															if param.ndim == 1:
																				# cast the small parameters (e.g. layernorm) to fp32 for stability
																				lowercase_					=      param.data.to(torch.floataa							)

																				 # Step 2: add adapters
										for _, module in model.named_modules():
															if "OPTAttention" in repr(type(lowercase_							)							):
																				lowercase_					=      LoRALayer(module.q_proj				,       rank=16							)
																				lowercase_					=      LoRALayer(module.k_proj				,       rank=16							)
																				lowercase_					=      LoRALayer(module.v_proj				,       rank=16							)

																				 # Step 3: dummy batch
										lowercase_					=      self.tokenizer("""Test batch """				,       return_tensors="""pt"""							).to(0							)

										# Step 4: Check if the gradient is not None
										with torch.cuda.amp.autocast():
															lowercase_					=      model.forward(**lowercase_							)
															out.logits.norm().backward()

										for module in model.modules():
															if isinstance(lowercase_				,       lowercase_							):
																				self.assertTrue(module.adapter[1].weight.grad is not None							)
																				self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
															elif isinstance(lowercase_				,       nn.Embedding							):
																				self.assertTrue(module.weight.grad is None							)







class 							UpperCamelCase__ (						__magic_name__ ):
					__SCREAMING_SNAKE_CASE							:	Optional[int]            = 'gpt2-xl'
					__SCREAMING_SNAKE_CASE							:	Optional[int]            = 3.3_1_9_1_8_5_4_8_5_4_1_5_2_1_8_7



from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = None    ,     a__ = False    ,     )      ->tuple[int, float, str]:




  '''simple docstring'''

  _UpperCamelCase           =    cipher_alphabet or [chr(a__ ) for i in range(97    ,     123 )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase           =    {
        "a": 0.08497,
        "b": 0.01492,
        "c": 0.02202,
        "d": 0.04253,
        "e": 0.11162,
        "f": 0.02228,
        "g": 0.02015,
        "h": 0.06094,
        "i": 0.07546,
        "j": 0.00153,
        "k": 0.01292,
        "l": 0.04025,
        "m": 0.02406,
        "n": 0.06749,
        "o": 0.07507,
        "p": 0.01929,
        "q": 0.00095,
        "r": 0.07587,
        "s": 0.06327,
        "t": 0.09356,
        "u": 0.02758,
        "v": 0.00978,
        "w": 0.02560,
        "x": 0.00150,
        "y": 0.01994,
        "z": 0.00077,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase           =    frequencies_dict

  if not case_sensitive:
    _UpperCamelCase           =    ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase           =    {}

  # cycle through all of the shifts
  for shift in range(len(a__ ) ):
    _UpperCamelCase           =    ""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase           =    (alphabet_letters.index(letter.lower() ) - shift) % len(
            a__ )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase           =    0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase           =    letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.lower().count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase           =    (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase           =    min(
      a__    ,     key=a__    ,     )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )      ,						(
      _UpperCamelCase
  )      ,						
  )           =    chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )




